Load Profiling in Distributed Real-Time Systems

Load balancing is often used to ensure that nodes in a distributed systems are equally loaded. In this paper, we show that for real-time systems, load balancing is not desirable. In particular, we propose a new load-profiling strategy that allows the nodes of a distributed system to be unequally loaded. Using load profiling, the system attempts to distribute the load amongst its nodes so as to maximize the chances of finding a node that would satisfy the computational needs of incoming real-time tasks. To that end, we describe and evaluate a distributed load-profiling protocol for dynamically scheduling time-constrained tasks in a loosely-coupled distributed environment. When a task is submitted to a node, the scheduling software tries to schedule the task locally so as to meet its deadline. If that is not feasible, it tries to locate another node where this could be done with a high probability of success, while attempting to maintain an overall load profile for the system. Nodes in the system inform each other about their state using a combination of multicasting and gossiping. The performance of the proposed protocol is evaluated via simulation, and is contrasted to other dynamic scheduling protocols for real-time distributed systems. Based on our findings, we argue that keeping a diverse availability profile and using passive bidding (through gossiping) are both advantageous to distributed scheduling for real-time systems.National Science Foundation (CCR-9308344

Inc-part: incremental partitioning for load balancing in large-scale behavioral simulations

Large-scale behavioral simulations are widely used to study real-world multi-agent systems. Such programs normally run in discrete time-steps or ticks, with simulated space decomposed into domains that are distributed over a set of workers to achieve parallelism. A distinguishing feature of behavioral simulations is their frequent and high-volume group migration, the phenomenon in which simulated objects traverse domains in groups at massive scale in each tick. This results in continual and significant load imbalance among domains. To tackle this problem, traditional load balancing approaches either require excessive load re-profiling and redistribution, which lead to high computation/communication costs, or perform poorly because their statically partitioned data domains cannot reflect load changes brought by group migration. In this paper, we propose an effective and low-cost load balancing scheme, named Inc-part, based on a key observation that an object is unlikely to move a long distance (across many domains) within a single tick. This localized mobility property allows one to efficiently estimate the load of a dynamic domain incrementally, based on merely the load changes occurring in its neighborhood. The domains experiencing significant load changes are then partitioned or merged, and redistributed to redress load imbalance among the workers. Experiments on a 64-node (1,024-core) platform show that Inc-part can attain excellent load balance with dramatically lowered costs compared to state-of-the-art solutions

Distributed generation on rural electricity networks - a lines company perspective : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Energy Management at Massey University

CD held with Reference copyA number of electricity assets used in rural New Zealand yield a very low return on investment. According to the provisions of the Electricity Act 1992, after 01 April 2013, lines companies may terminate supply to any customer to whom they cannot provide electricity lines services profitably. This research was undertaken to assist the policy makers, lines companies, rural investors on the viability of distributed generation in a rural setting from the point of view of the lines company and the investor as well as to provide recommendations to the problem areas. A dynamic distributed generation model was developed to simulate critical distributed generation scenarios relevant to New Zealand, such as diverse metering arrangements, time dependent electricity prices, peak shaving by load control, peak lopping by dispatchable distributed generation and state subsides, which are not addressed in commercial software. Data required to run the model was collected from a small rural North Island sheep and beef farming community situated at the end of a 26km long radial distribution feeder. Additional operational data were also collected from the community on distributed resources such as solar hot water systems. A number of optimum distributed generation combinations involving a range of technologies under different metering arrangements and price signals were identified for the small and the medium investor. The effect of influencing factors, such as state initiatives and technological growth, on the investor and the lines companies were discussed. Recommendations for future implementation in order to integrate distributed generation on to rural networks were also given. Several key research areas were identified and discussed including low cost micro hydro, wind resource assessment, diversification of the use of the induction generators, voltage flicker and dynamic distributed generation techno-economic forecasting tools